Traditional dynamic models of continuum robots
are in general computationally expensive and not suitable
for real-time control. Recent approaches using learning-based
methods to approximate the dynamic model of continuum
robots for control have been promising, although real data
hungry—which may cause potential damage to robots and
be time consuming—and getting poorer performance when
trained with simulation data only. This paper presents a modelbased learning framework for continuum robot closed-loop
control that, by combining simulation and real data, shows
to require only 100 real data to outperform a real-data-only
controller trained using up to 10000 points. The introduced
data-efficient framework with three control policies has utilized
a Gaussian process regression (GPR) and a recurrent neural
network (RNN). Control policy A uses a GPR model and a RNN
trained in simulation to optimize control outputs for simulated
targets; control policy B retrains the RNN in policy A with data
generated from the GPR model to adapt to real robot physics;
control policy C utilizes policy A and B to form a hybrid policy.
Using a continuum robot with soft spines, we show that our
approach provides an efficient framework to bridge the sim-to-real gap in model-based learning for continuum robots.